scholarly journals Multi-Temporal Change Detection Analysis of Vertical Sprawl over Limassol City Centre and Amathus Archaeological Site in Cyprus during 2015–2020 Using the Sentinel-1 Sensor and the Google Earth Engine Platform

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1884
Author(s):  
Athos Agapiou
Keyword(s):  
Change Detection ◽  
Satellite Images ◽  
Archaeological Site ◽  
Google Earth ◽  
City Centre ◽  
Time Interval ◽  
High Resolution Satellite Images ◽  
Multi Temporal ◽  
Google Earth Engine ◽  
The City

Urban sprawl can negatively impact the archaeological record of an area. In order to study the urbanisation process and its patterns, satellite images were used in the past to identify land-use changes and detect individual buildings and constructions. However, this approach involves the acquisition of high-resolution satellite images, the cost of which is increases according to the size of the area under study, as well as the time interval of the analysis. In this paper, we implemented a quick, automatic and low-cost exploration of large areas, for addressing this purpose, aiming to provide at a medium resolution of an overview of the landscape changes. This study focuses on using radar Sentinel-1 images to monitor and detect multi-temporal changes during the period 2015–2020 in Limassol, Cyprus. In addition, the big data cloud platform, Google Earth Engine, was used to process the data. Three different change detection methods were implemented in this platform as follow: (a) vertical transmit, vertical receive (VV) and vertical transmit, horizontal receive (VH) polarisations pseudo-colour composites; (b) the Rapid and Easy Change Detection in Radar Time-Series by Variation Coefficient (REACTIV) Google Earth Engine algorithm; and (c) a multi-temporal Wishart-based change detection algorithm. The overall findings are presented for the wider area of the Limassol city, with special focus on the archaeological site of “Amathus” and the city centre of Limassol. For validation purposes, satellite images from the multi-temporal archive from the Google Earth platform were used. The methods mentioned above were able to capture the urbanization process of the city that has been initiated during this period due to recent large construction projects.

scholarly journals Image Fusion-Based Land Cover Change Detection Using Multi-Temporal High-Resolution Satellite Images

2017 ◽  
Vol 9 (8) ◽  
pp. 804 ◽  
Author(s):  
Biao Wang ◽  
Jaewan Choi ◽  
Seokeun Choi ◽  
Soungki Lee ◽  
Penghai Wu ◽  
...  
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Change Detection ◽  
Image Fusion ◽  
Land Cover Change ◽  
Satellite Images ◽  
High Resolution Satellite Images ◽  
Multi Temporal ◽  
Land Cover Change Detection

scholarly journals AUTOMATIC URBAN ILLEGAL BUILDING DETECTION USING MULTI-TEMPORAL SATELLITE IMAGES AND GEOSPATIAL INFORMATION SYSTEMS

2015 ◽  
Vol XL-1-W5 ◽  
pp. 387-393 ◽  
Author(s):  
N. Khalili Moghadam ◽  
M. R. Delavar ◽  
P. Hanachee
Keyword(s):  
Change Detection ◽  
Satellite Images ◽  
Building Detection ◽  
Existing Buildings ◽  
Geospatial Information ◽  
Processing Step ◽  
Multi Temporal ◽  
Under Construction ◽  
Processing Techniques ◽  
The City

With the unprecedented growth of urban population and urban development, we are faced with the growing trend of illegal building (IB) construction. Field visit, as the currently used method of IB detection, is time and man power consuming, in addition to its high cost. Therefore, an automatic IB detection is required. Acquiring multi-temporal satellite images and using image processing techniques for automatic change detection is one of the optimum methods which can be used in IB monitoring. In this research an automatic method of IB detection has been proposed. Two-temporal panchromatic satellite images of IRS-P5 of the study area in a part of Tehran, the city map and an updated spatial database of existing buildings were used to detect the suspected IBs. In the pre-processing step, the images were geometrically and radiometrically corrected. In the next step, the changed pixels were detected using K-means clustering technique because of its quickness and less user’s intervention required. Then, all the changed pixels of each building were identified and the change percentage of each building with the standard threshold of changes was compared to detect the buildings which are under construction. Finally, the IBs were detected by checking the municipality database. The unmatched constructed buildings with municipal database will be field checked to identify the IBs. The results show that out of 343 buildings appeared in the images; only 19 buildings were detected as under construction and three of them as unlicensed buildings. Furthermore, the overall accuracies of 83%, 79% and 75% were obtained for K-means change detection, detection of under construction buildings and IBs detection, respectively.

scholarly journals Assessing the Effect of Training Sampling Design on the Performance of Machine Learning Classifiers for Land Cover Mapping Using Multi-Temporal Remote Sensing Data and Google Earth Engine

2021 ◽  
Vol 13 (8) ◽  
pp. 1433
Author(s):  
Shobitha Shetty ◽  
Prasun Kumar Gupta ◽  
Mariana Belgiu ◽  
S. K. Srivastav
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Random Sampling ◽  
Sampling Design ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Multi Temporal ◽  
Google Earth Engine

Machine learning classifiers are being increasingly used nowadays for Land Use and Land Cover (LULC) mapping from remote sensing images. However, arriving at the right choice of classifier requires understanding the main factors influencing their performance. The present study investigated firstly the effect of training sampling design on the classification results obtained by Random Forest (RF) classifier and, secondly, it compared its performance with other machine learning classifiers for LULC mapping using multi-temporal satellite remote sensing data and the Google Earth Engine (GEE) platform. We evaluated the impact of three sampling methods, namely Stratified Equal Random Sampling (SRS(Eq)), Stratified Proportional Random Sampling (SRS(Prop)), and Stratified Systematic Sampling (SSS) upon the classification results obtained by the RF trained LULC model. Our results showed that the SRS(Prop) method favors major classes while achieving good overall accuracy. The SRS(Eq) method provides good class-level accuracies, even for minority classes, whereas the SSS method performs well for areas with large intra-class variability. Toward evaluating the performance of machine learning classifiers, RF outperformed Classification and Regression Trees (CART), Support Vector Machine (SVM), and Relevance Vector Machine (RVM) with a >95% confidence level. The performance of CART and SVM classifiers were found to be similar. RVM achieved good classification results with a limited number of training samples.

scholarly journals Accelerate disaster risk mapping using an automated zonal change detection model for loss estimation and recovery: a flood case study

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Ali Amasha
Keyword(s):  
Change Detection ◽  
Satellite Images ◽  
Flash Flood ◽  
Disaster Risk ◽  
Loss Estimation ◽  
Risk Mapping ◽  
Detection Model ◽  
Applied Model ◽  
High Resolution Satellite Images ◽  
Before And After

Abstract Background The flash flood still constitutes one of the major natural meteorological disasters harmfully threatening local communities, that creates life losses and destroying infrastructures. The severity and magnitude of disasters always reflected from the size of impacts. Most of the conventional research models related to flooding vulnerability are focusing on hydro-meteorological and morphometric measurements. It, however, requires quick estimate of the flood losses and assess the severity using reliable information. An automated zonal change detection model applied, using two high-resolution satellite images dated 2009 and 2011 coupled with LU/LC GIS layer, on western El-Arish City, downstream of Wadi El-Arish basin. The model enabled to estimate the severity of a past flood incident in 2010. Results The model calculated the total changes based on the before and after satellite images based on pixel-by-pixel comparison. The estimated direct-damages nearly 32,951 m2 of the total mapped LU/LC classes; (e.g., 11,407 m2 as 3.17% of the cultivated lands; 6031 m2 as 7.22% of the built-up areas and 4040 m2 as 3.62% of the paved roads network). The estimated cost of losses, in 2010 economic prices for the selected three LU/LC classes, is nearly 25 million USD, for the cultivation fruits and olives trees, ~ 4 million USD for built-up areas and ~ 1 million USD for paved roads network. Conclusion The disasters’ damage and loss estimation process takes many detailed data, longtime, and costed as well. The applied model accelerates the disaster risk mapping that provides an informative support for loss estimation. Therefore, decision-makers and professionals need to apply this model for quick the disaster risks management and recovery.

scholarly journals Google Earth Engine as Multi-Sensor Open-Source Tool for Supporting the Preservation of Archaeological Areas: The Case Study of Flood and Fire Mapping in Metaponto, Italy

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1791
Author(s):  
Carmen Fattore ◽  
Nicodemo Abate ◽  
Farid Faridani ◽  
Nicola Masini ◽  
Rosa Lasaponara
Keyword(s):  
Open Source ◽  
Archaeological Site ◽  
Unsupervised Classification ◽  
Google Earth ◽  
Heritage Sites ◽  
Cultural Properties ◽  
Google Earth Engine ◽  
Archaeological Areas ◽  
The Impact

In recent years, the impact of Climate change, anthropogenic and natural hazards (such as earthquakes, landslides, floods, tsunamis, fires) has dramatically increased and adversely affected modern and past human buildings including outstanding cultural properties and UNESCO heritage sites. Research about protection/monitoring of cultural heritage is crucial to preserve our cultural properties and (with them also) our history and identity. This paper is focused on the use of the open-source Google Earth Engine tool herein used to analyze flood and fire events which affected the area of Metaponto (southern Italy), near the homonymous Greek-Roman archaeological site. The use of the Google Earth Engine has allowed the supervised and unsupervised classification of areas affected by flooding (2013–2020) and fire (2017) in the past years, obtaining remarkable results and useful information for setting up strategies to mitigate damage and support the preservation of areas and landscape rich in cultural and natural heritage.

scholarly journals Multi-temporal Satellite Images Change Detection Algorithm Based on NSCT

2011 ◽  
Vol 24 ◽  
pp. 252-256 ◽  
Author(s):  
Wei Cui ◽  
Zhenhong Jia ◽  
Xizhong Qin ◽  
Jie Yang ◽  
Yingjie Hu
Keyword(s):  
Change Detection ◽  
Satellite Images ◽  
Detection Algorithm ◽  
Multi Temporal

Regional-scale analysis of dune-beach systems using Google Earth Engine

2021 ◽  
Author(s):  
Melissa Latella ◽  
Arjen Luijendijk ◽  
Carlo Camporeale
Keyword(s):  
Ecosystem Services ◽  
Satellite Images ◽  
Regional Scale ◽  
Google Earth ◽  
Classification Algorithm ◽  
Mitigation Strategies ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Beach Width ◽  
Google Earth Engine

<p>Coastal sand dunes provide a large variety of ecosystem services, among which the inland protection from marine floods. Nowadays, this protection is fundamental, and its importance will further increase in the future due to the rise of the sea level and storm violence induced by climate change. Despite the crucial role of coastal dunes and their potential application in mitigation strategies, the phenomenon of the coastal squeeze, which is mainly caused by the urban sprawl, is progressively reducing the extents of the areas where dune can freely undergo their dynamics, thus dramatically impairing their capability of providing ecosystem services.</p><p>Aiming to embed the use of satellite images in the study of coastal foredune and beach dynamics, we developed a classification algorithm that uses the satellite images and server-side functions of Google Earth Engine (GEE). The algorithm runs on the GEE Python API and allows the user to retrieve all the available images for the study site and the chosen time period from the selected sensor collection. The algorithm also filters the cloudy and saturated pixels and creates a percentile-composite image over which it applies a random forest classification algorithm. The classification is finally refined by defining a mask for land pixels only. </p><p>According to the provided training data and sensor selection, the algorithm can give different outcomes, ranging from sand and vegetation maps, beach width measurements, and shoreline time evolution visualization. This very versatile tool that can be used in a great variety of applications within the monitoring and understanding of the dune-beach systems and associated coastal ecosystem services. For instance, we show how this algorithm, combined with machine learning techniques and the assimilation of real data, can support the calibration of a coastal model that gives the natural extent of the beach width and that can be, therefore, used to plan restoration activities. </p>

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